Ecology

Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the “landscape of fear” in Yellowstone National Park, U.S.A.. Can. J. Zool. 79, 1401–1409 (2001).Article 

Google Scholar 
Laundré, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: Ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).Article 

Google Scholar 
Suraci, J. P., Clinchy, M., Zanette, L. Y. & Wilmers, C. C. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecol. Lett. 22, 1578–1586 (2019).Article 

Google Scholar 
Miller, S. G., Knight, R. L. & Miller, C. K. Wildlife responses to pedestrians and dogs. Wildl. Soc. B. 29, 124–132 (2001).
Google Scholar 
Larson, C., Reed, S., Merenlender, A. M. & Crooks, K. R. Effects of recreation on animals revealed as widespread through a global systemic review. PLoS ONE 11, 1–21 (2016).Article 

Google Scholar 
Balmford, A. et al. Walk on the wild side: Estimating the global magnitude of visits to protected areas. PLoS Biol 13, 1–6 (2015).Article 

Google Scholar 
Baker, A. D. & Leberg, P. L. Impacts of human recreation on carnivores in protected areas. PLoS Biol 13, 1–21 (2018).
Google Scholar 
Schulze, K. et al. An assessment of threats to terrestrial protected areas. Cons. Lett. 11, 1–10 (2018).Article 

Google Scholar 
Suraci, J. P. et al. Disturbance type and species life history predict mammal responses to humans. Glob. Change Biol. 27, 3718–3731 (2021).Article 
CAS 

Google Scholar 
Reilly, M. L., Tobler, M. W., Sonderegger, D. L. & Beier, P. Spatial and temporal response of wildlife to recreational activities in the San Francisco Bay ecoregion. Biol. Conserv. 207, 117–126 (2017).Article 

Google Scholar 
Naidoo, R. & Burton, A. C. Relative effects of recreational activities on a temperate terrestrial wildlife assemblage. Conserv. Sci. Pract. 2, e271 (2020).
Google Scholar 
Nickel, B. A., Suraci, J. P., Allen, M. L. & Wilmers, C. C. Human presence and human footprint have non-equivalent effects on wildlife spatiotemporal habitat use. Biol. Conserv. 241, 1–11 (2020).Article 

Google Scholar 
Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063 (2020).Article 

Google Scholar 
Poggiato, G. et al. On the interpretations of joint modeling in community ecology. TREE 36, 391–401 (2021).
Google Scholar 
Bates, A. E., Primack, R. B., Moraga, P. & Duarte, C. M. COVID-19 pandemic and associated lockdown as a “Global Human Confinement Experiment” to investigate biodiversity conservation. Biol. Conserv. 248, 1–6 (2020).Article 

Google Scholar 
Rutz, C. et al. COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife. Nat. Ecol. Evol. 4, 1156–1159 (2020).Article 

Google Scholar 
Wang, Y., Allen, M. L. & Wilmers, C. C. Mesopredator spatial and temporal responses to large predators and human development in the Santa Cruz Mountains of California. Biol. Conserv. 190, 23–33 (2015).Article 

Google Scholar 
Lewis, J. S. et al. Human activity influences wildlife populations and activity patterns: implications for spatial and temporal refuges. Ecosphere 12, 1–16 (2021).Article 

Google Scholar 
Corradini, A. et al. Effects of cumulated outdoor activity on wildlife habitat use. Biol. Conserv. 253, 108818 (2021).Article 

Google Scholar 
Soule, M. E. et al. Dynamics of rapid extinctions of chaparral-requiring birds in urban habitat islands. Conserv. Biol. 2, 75–92 (1988).Article 

Google Scholar 
Feit, B., Feit, A. & Letnic, M. Apex predators decouple population dynamics between mesopredators and their prey. Ecosystems 22, 1606–1617 (2019).Article 

Google Scholar 
Berger, J. Fear, human shields and the redistribution of prey and predators in protected areas. Biol. Lett. 3, 620–623 (2007).Article 

Google Scholar 
Sarmento, W., Biel, M. & Berger, J. Redistribution, human shields and loss of migratory behavior in the crown of the continent. Intermt. J. Sci. 22, 2016 (2016).
Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).Article 
ADS 
CAS 

Google Scholar 
Microsoft. AI for Earth camera trap image processing API (2020).Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapR: an R package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462 (2016).Article 

Google Scholar 
MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G. & Franklin, A. B. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207. https://doi.org/10.1890/02-3090 (2003).Article 

Google Scholar 
MacKenzie, D. I. et al. Occupancy estimation and modeling (Elsevier, 2018).
Google Scholar 
Fiske, I. & Chandler, R. unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Soft. 4, 1–23 (2011).
Google Scholar 
Mazerolle, M. J. AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R package version 2.3-1 (2020). https://cran.r-project.org/package=AICcmodavg.Ladle, A., Steenweg, R., Shepherd, B. & Boyce, M. S. The role of human outdoor recreation in shaping patterns of grizzly bear-black bear co-occurrence. PLoS ONE 13, 1–16 (2018).Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337 (2009).Article 
MathSciNet 
MATH 

Google Scholar 
Agostinelli, C. & Lund, U. R package ‘circular’: circular statistics (version 0.4-94.1 (2022). https://r-forge.r-project.org/projects/circular/.Santos, F. et al. Prey availability and temporal partitioning modulate felid coexistence in Neotropical forests. PLoS ONE 14, 1–23 (2019).Article 

Google Scholar 
Olea, P. P., Iglesias, N. & Mateo-Tomás, P. Temporal resource partitioning mediates vertebrate coexistence at carcasses: the role of competitive and facilitative interactions. Basic Appl. Ecol. 60, 63–75 (2022).Article 

Google Scholar 
Shilling, F. et al. A reprieve from US wildlife mortality on roads during the COVID-19 pandemic. Biol. Conserv. 256, 109013 (2021).Article 

Google Scholar 
Behera, A. K. et al. The impacts of COVID-19 lockdown on wildlife in Deccan Plateau. India. Sci. Total Environ. 822, 153268 (2022).Article 
ADS 
CAS 

Google Scholar 
Procko, M., Naidoo, R., LeMay, V. & Burton, A. C. Human impacts on mammals in and around a protected area before, during, and after COVID-19 lockdowns. Conserv. Sci. Pract. 4, e12743. https://doi.org/10.1111/csp2.12743 (2022).Article 

Google Scholar 
Sanderfoot, O. V., Kaufman, J. D. & Gardner, B. Drivers of avian habitat use and detection of backyard birds in the Pacific Northwest during COVID-19 pandemic lockdowns. Sci. Rep. 12, 12655 (2022).Article 
ADS 
CAS 

Google Scholar 
Nevin, J. A. & Grace, R. C. Behavioral momentum and the law of effect. Behav. Brain Sci. 23, 73–90 (2000).Article 
CAS 

Google Scholar 
Kautz, T. M. et al. Large carnivore response to human road use suggests a landscape of coexistence. Glob. Ecol. Conserv. 30, e01772 (2021).Article 

Google Scholar 
Frey, S., Volpe, J. P., Heim, N. A., Paczkowski, J. & Fisher, J. T. Move to nocturnality not a universal trend in carnivore species on disturbed landscapes. Oikos 129, 1128–1140 (2020).Article 

Google Scholar 
Schwartz, C. C. et al. Contrasting activity patterns of sympatric and allopatric black and grizzly bears. J. Wildl. Manag. 74, 1628–1638 (2010).Article 

Google Scholar 
Fortin, J. K. et al. Impacts of human recreation on brown bears (Ursus arctos): a review and new management tool. PLoS ONE 11, 1–26 (2016).Article 

Google Scholar 
Kendall, K. C. et al. Grizzly bear density in Glacier National Park. Montana. J. Wildl. Manag. 72, 1693–1705 (2008).Article 

Google Scholar 
Stetz, J. B., Kendall, K. C. & Macleod, A. C. Black bear density in Glacier National Park, Montana. Wildl. Soc. Bull. 38, 60–70 (2014).Article 

Google Scholar 
Sargeant, A. B. & Allen, S. H. Observed interactions between coyotes and red foxes. J. Mamm. 70, 631–633 (1989).Article 

Google Scholar 
Newsome, T. M. & Ripple, W. J. A continental scale trophic cascade from wolves through coyotes to foxes. J. Anim. Ecol. 84, 49–59 (2015).Article 

Google Scholar 
Naylor, L. M., Wisdom, M. J. & Anthony, R. G. Behavioral responses of North American elk to recreational activity. J. Wildl. Manag. 73, 328–338 (2009).Article 

Google Scholar 
Sarmento, W. M. & Berger, J. Human visitation limits the utility of protected areas as ecological baselines. Biol. Conserv. 212, 316–326 (2017).Article 

Google Scholar 
Garber, S. D. & Burger, J. A 20-year study documenting the relationship between turtle decline and human recreation. Ecol. Appl. 5, 1151–1162 (1995).Article 

Google Scholar 
Winter, P. L., Selin, S., Cerveny, L. & Bricker, K. Outdoor recreation, nature-based tourism, and sustainability. Sustainability 12, 1–12 (2020).
Google Scholar 
Geffroy, B., Samia, D. S. M., Bessa, E. & Blumstein, D. T. How nature-based tourism might increase prey vulnerability to predators. TREE 30, 755–765 (2015).
Google Scholar 
Eagles, P. F. J., McCool, S. F. & Haynes, C. D. Sustainable Tourism in Protected Areas: Guidelines for Planning and Management Vol. 8 (IUCN, 2002).Book 

Google Scholar  More

Cavanaugh CM, McKiness ZP, Newton ILG, Stewart FJ. Marine chemosynthetic symbioses. Prokaryotes. 2006;1:475–507.Article 

Google Scholar 
Beinart RA, Luo C, Konstantinidis KT, Stewart FJ, Girguis PR. The bacterial symbionts of closely related hydrothermal vent snails with distinct geochemical habitats show broad similarity in chemoautotrophic gene content. Front Microbiol. 2019;10:1818.Article 

Google Scholar 
Robidart JC, Bench SR, Feldman RA, Novoradovsky A, Podell SB, Gaasterland T, et al. Metabolic versatility of the Riftia pachyptila endosymbiont revealed through metagenomics. Environ Microbiol. 2008;10:727–37.Article 
CAS 

Google Scholar 
Ponnudurai R, Sayavedra L, Kleiner M, Heiden SE, Thürmer A, Felbeck H, et al. Genome sequence of the sulfur-oxidizing Bathymodiolus thermophilus gill endosymbiont. Stand Genom Sci. 2017;12:50.Article 

Google Scholar 
Duperron S, Bergin C, Zielinski F, Blazejak A, Pernthaler A, McKiness ZP, et al. A dual symbiosis shared by two mussel species, Bathymodiolus azoricus and Bathymodiolus puteoserpentis (Bivalvia: Mytilidae), from hydrothermal vents along the northern Mid-Atlantic Ridge. Environ Microbiol. 2006;8:1441–7.Article 
CAS 

Google Scholar 
Dubilier N, Bergin C, Lott C. Symbiotic diversity in marine animals: the art of harnessing chemosynthesis. Nat Rev Microbiol. 2008;6:725–40.Article 
CAS 

Google Scholar 
Sogin EM, Leisch N, Dubilier N. Chemosynthetic symbioses. Curr Biol. 2020;30:R1137–R1142.Article 
CAS 

Google Scholar 
Roeselers G, Newton ILG. On the evolutionary ecology of symbioses between chemosynthetic bacteria and bivalves. Appl Microbiol Biotechnol. 2012;94:1–10.Article 
CAS 

Google Scholar 
Moran NA. Symbiosis as an adaptive process and source of phenotypic complexity. Proc Natl Acad Sci USA. 2007;104 Suppl 1:8627–33.Article 
CAS 

Google Scholar 
McMullen JG, Peterson BF, Forst S, Blair HG, Patricia Stock S. Fitness costs of symbiont switching using entomopathogenic nematodes as a model. BMC Evol Biol. 2017;17. https://doi.org/10.1186/s12862-017-0939-6.Taylor JD, Glover E. Biology, evolution and generic review of the chemosymbiotic bivalve family Lucinidae. London, UK: Ray Society; 2021.Osvatic JT, Wilkins LGE, Leibrecht L, Leray M, Zauner S, Polzin J, et al. Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups. Proc Natl Acad Sci USA. 2021;118. https://doi.org/10.1073/pnas.2104378118.Petersen JM, Kemper A, Gruber-Vodicka H, Cardini U, van der Geest M, Kleiner M, et al. Chemosynthetic symbionts of marine invertebrate animals are capable of nitrogen fixation. Nat Microbiol. 2016;2:16195.Article 
CAS 

Google Scholar 
Lim SJ, Davis B, Gill D, Swetenburg J, Anderson LC, Engel AS, et al. Gill microbiome structure and function in the chemosymbiotic coastal lucinid Stewartia floridana. FEMS Microbiol Ecol. 2021;97. https://doi.org/10.1093/femsec/fiab042.Lim SJ, Davis BG, Gill DE, Walton J, Nachman E, Engel AS, et al. Taxonomic and functional heterogeneity of the gill microbiome in a symbiotic coastal mangrove lucinid species. ISME J. 2019;13:902–20.Article 
CAS 

Google Scholar 
Gros O, Liberge M, Felbeck H. Interspecific infection of aposymbiotic juveniles of Codakia orbicularis by various tropical lucinid gill-endosymbionts. Mar Biol. 2003;142:57–66.Article 

Google Scholar 
Gros O, Elisabeth NH, Gustave SDD, Caro A, Dubilier N. Plasticity of symbiont acquisition throughout the life cycle of the shallow-water tropical lucinid Codakia orbiculata (Mollusca: Bivalvia). Environ Microbiol. 2012;14:1584–95.Article 
CAS 

Google Scholar 
Gros O, Frenkiel L, Mouëza M. Embryonic, larval, and post-larval development in the symbiotic clam Codakia orbicularis (Bivalvia: Lucinidae). Invertebr Biol. 1997;116:86–101.Article 

Google Scholar 
König S, Gros O, Heiden SE, Hinzke T, Thürmer A, Poehlein A, et al. Nitrogen fixation in a chemoautotrophic lucinid symbiosis. Nat Microbiol. 2016;2:16193.Article 

Google Scholar 
Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol. 2010;18:455–63.Article 
CAS 

Google Scholar 
Cardini U, Bednarz VN, Foster RA, Wild C. Benthic N2 fixation in coral reefs and the potential effects of human-induced environmental change. Ecol Evol. 2014;4:1706–27.Article 

Google Scholar 
Glover EA, Taylor JD. Lucinidae of the Philippines: highest known diversity and ubiquity of chemosymbiotic bivalves from intertidal to bathyal depths (Mollusca: Bivalvia). mém Mus Natl Hist Nat. 2016;208:65–234.
Google Scholar 
Taylor JD, Glover EA, Williams ST. Diversification of chemosymbiotic bivalves: origins and relationships of deeper water Lucinidae. Biol J Linn Soc Lond. 2014;111:401–20.Article 

Google Scholar 
von Cosel R. Taxonomy of tropical West African bivalves. VI. Remarks on Lucinidae (Mollusca, Bivalvia), with description of six new genera and eight new species. Zoosystema. 2006;28:805.
Google Scholar 
Glover EA, Taylor JD, Rowden AA. Bathyaustriella thionipta, a new lucinid bivalve from a hydrothermal vent on the Kermadec Ridge, New Zealand and its relationship to shallow-water taxa (Bivalvia: Lucinidae). J Mollusca Stud. 2004;70:283–95.Article 

Google Scholar 
Paulus E Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front Mar Sci. 2021;8. https://doi.org/10.3389/fmars.2021.667048.Brown A, Thatje S. Explaining bathymetric diversity patterns in marine benthic invertebrates and demersal fishes: physiological contributions to adaptation of life at depth. Biol Rev Camb Philos Soc. 2014;89:406–26.Article 

Google Scholar 
Smith CR, De Leo FC, Bernardino AF, Sweetman AK, Arbizu PM. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol Evol. 2008;23:518–28.Article 

Google Scholar 
Gage JD, Tyler PA. Deep-sea biology: a natural history of organisms at the deep-sea floor. Cambridge, UK: Cambridge University Press; 1991.Iken K, Brey T, Wand U, Voigt J, Junghans P. Food web structure of the benthic community at the Porcupine Abyssal Plain (NE Atlantic): a stable isotope analysis. Prog Oceanogr. 2001;50:383–405.Article 

Google Scholar 
von Cosel R, Bouchet P. Tropical deep-water lucinids (Mollusca: Bivalvia) from the Indo-Pacific: essentially unknown, but diverse and occasionally gigantic. mém Mus Natl Hist Nat. 2008;196:115–213.
Google Scholar 
Stearns REC Scientific results of explorations by the US Fish Commission steamer Albatross. No. XVII. Descriptions of new West American land, fresh-water, and marine shells, with notes and comments. Proceedings of the United States National Museum. 1890. https://repository.si.edu/bitstream/handle/10088/13174/1/USNMP-13_813_1890.pdf.Taylor JD, Glover EA. The lucinid bivalve genus Cardiolucina (Mollusca, Bivalvia, Lucinidae): systematics, anatomy and relationships. Bull Br Mus Nat Hist Zoo. 1997;63:93–122.
Google Scholar 
Coan EV, Valentich-Scott P, Sadeghian PS. Bivalve seashells of tropical West America: marine bivalve mollusks from Baja California to Northern Peru. Santa Barbara, USA: Museum of Natural History; 2012.von Cosel R, Gofas S. Marine bivalves of tropical West Africa: from Rio de Oro to southern Angola. Marseille, France: Muséum national d’Histoire naturelle, Paris; 2019. p 1104.Atkinson L, Sink K. Field guide to the offshore marine invertebrates of South Africa. 2018. https://doi.org/10.15493/SAEON.PUB.10000001.Montagu G. Testacea Britannica, or natural history of British shells. London, UK: JS Hollis; 1803.Taylor J, Glover E. New lucinid bivalves from shallow and deeper water of the Indian and West Pacific Oceans (Mollusca, Bivalvia, Lucinidae). ZooKeys. 2013;326:69–90.Article 

Google Scholar 
Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.Article 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.Article 
CAS 

Google Scholar 
Pjevac P, Hausmann B, Schwarz J, Kohl G, Herbold CW, Loy A, et al. An economical and flexible dual barcoding, two-step PCR approach for highly multiplexed amplicon sequencing. Front Microbiol. 2021;12:669776.Article 

Google Scholar 
McLaren MR, Callahan BJ. Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4587955.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.Article 
CAS 

Google Scholar 
Andersen KS, Kirkegaard RH, Karst SM, Albertsen M. ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. 2018. https://www.biorxiv.org/content/10.1101/299537v1.Bushnell B. BBMap: a fast, accurate, splice-aware aligner. Berkeley, CA, USA: Lawrence Berkeley National Lab. (LBNL); 2014.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.Article 
CAS 

Google Scholar 
Nurk S, Bankevich A, Antipov D, Gurevich A, Korobeynikov A, Lapidus A, et al. Assembling genomes and mini-metagenomes from highly chimeric reads. In: Research in Computational Molecular Biology. Springer Berlin Heidelberg; 2013. p. 158–70.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.Article 

Google Scholar 
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.Article 

Google Scholar 
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.Article 
CAS 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.Article 

Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
CAS 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019. https://doi.org/10.1093/bioinformatics/btz848.Parks DH, Chuvochina M, Chaumeil P-A, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.Article 
CAS 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.Article 
CAS 

Google Scholar 
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.Article 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.Article 

Google Scholar 
Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.Article 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.Article 

Google Scholar 
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.Article 
CAS 

Google Scholar 
Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.Article 

Google Scholar 
Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–5.Article 
CAS 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–W296.Article 
CAS 

Google Scholar 
Varghese NJ, Mukherjee S, Ivanova N, Konstantinidis KT, Mavrommatis K, Kyrpides NC, et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 2015;43:6761–71.Article 
CAS 

Google Scholar 
Qin Q-L, Xie B-B, Zhang X-Y, Chen X-L, Zhou B-C, Zhou J, et al. A proposed genus boundary for the prokaryotes based on genomic insights. J Bacteriol. 2014;196:2210–5.Article 

Google Scholar 
Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–D314.Article 
CAS 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol Biol Evol. 2017;34:2115–22.Article 
CAS 

Google Scholar 
Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, Olsen GJ, et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep. 2015;5:8365.Article 

Google Scholar 
Mahram A, Herbordt MC. NCBI BLASTP on high-performance reconfigurable computing systems. ACM Trans Reconfigurable Technol Syst. 2015;7:1–20.Article 

Google Scholar 
Yang Z. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 1997;13:555–6.CAS 

Google Scholar 
Osvatic J, Wilkins L. Strength of selection scripts. FigShare. 2022;8. https://doi.org/10.6084/m9.figshare.20626746.v1.Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56:1919–25.Article 
CAS 

Google Scholar 
Lan Y, Sun J, Chen C, Sun Y, Zhou Y, Yang Y, et al. Hologenome analysis reveals dual symbiosis in the deep-sea hydrothermal vent snail Gigantopelta aegis. Nat Commun. 2021;12:1165.Article 
CAS 

Google Scholar 
Leray M, Wilkins LGE, Apprill A, Bik HM, Clever F, Connolly SR, et al. Natural experiments and long-term monitoring are critical to understand and predict marine host-microbe ecology and evolution. PLoS Biol. 2021;19:e3001322.Article 
CAS 

Google Scholar 
Petersen Jillian M, Yuen B, Alexandre G. The symbiotic ‘all-rounders’: partnerships between marine animals and chemosynthetic nitrogen-fixing bacteria. Appl Environ Microbiol 2020;87:e02129–20.Johnson KS, Childress JJ, Hessler RR, Sakamoto-Arnold CM, Beehler CL. Chemical and biological interactions in the Rose Garden hydrothermal vent field, Galapagos spreading center. Deep Sea Res A. 1988;35:1723–44.Article 

Google Scholar 
Kennicutt ME II, Brooks JM, Burke RA Jr. Hydrocarbon seepage, gas hydrates, and authigenic carbonate in the northwestern Gulf of Mexico. Offshore Technology Conference; 1989. https://doi.org/10.4043/5952-ms.Lilley MD, Butterfield DA, Olson EJ, Lupton JE, Macko SA, McDuff RE. Anomalous CH4 and NH4+ concentrations at an unsedimented mid-ocean-ridge hydrothermal system. Nature. 1993;364:45–47.Article 
CAS 

Google Scholar 
Von Damm KL, Edmond JM, Measures CI, Grant B. Chemistry of submarine hydrothermal solutions at Guaymas Basin, Gulf of California. Geochim Cosmochim Acta. 1985;49:2221–37.Article 

Google Scholar 
Lee RW, Thuesen EV, Childress JJ. Ammonium and free amino acids as nitrogen sources for the chemoautotrophic symbiosis Solemya reidi Bernard (Bivalvia: Protobranchia). J Exp Mar Bio Ecol. 1992;158:75–91.Article 
CAS 

Google Scholar 
Sanders JG, Beinart RA, Stewart FJ, Delong EF, Girguis PR. Metatranscriptomics reveal differences in in situ energy and nitrogen metabolism among hydrothermal vent snail symbionts. ISME J. 2013;7:1556–67.Article 
CAS 

Google Scholar 
Touchette BW, Burkholder JM. Review of nitrogen and phosphorus metabolism in seagrasses. J Exp Mar Bio Ecol. 2000;250:133–67.Article 
CAS 

Google Scholar 
Fourqurean JW, Zieman JC, Powell GVN. Relationships between porewater nutrients and seagrasses in a subtropical carbonate environment. Mar Biol. 1992;114:57–65.Article 
CAS 

Google Scholar 
Williams SL. Experimental studies of Caribbean seagrass bed development. Ecol Monogr. 1990;60:449–69.Article 

Google Scholar 
Herbert RA. Nitrogen cycling in coastal marine ecosystems. FEMS Microbiol Rev. 1999;23:563–90.Article 
CAS 

Google Scholar 
Risgaard-Petersen N, Dalsgaard T, Rysgaard S, Christensen PB, Borum J, McGlathery K, et al. Nitrogen balance of a temperate eelgrass Zostera marina bed. Mar Ecol Prog Ser. 1998;174:281–91.Article 
CAS 

Google Scholar 
Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.Article 
CAS 

Google Scholar 
Karthäuser C, Ahmerkamp S, Marchant HK, Bristow LA, Hauss H, Iversen MH, et al. Small sinking particles control anammox rates in the Peruvian oxygen minimum zone. Nat Commun. 2021;12:3235.Article 

Google Scholar 
Kuypers MMM, Lavik G, Woebken D, Schmid M, Fuchs BM, Amann R, et al. Massive nitrogen loss from the Benguela upwelling system through anaerobic ammonium oxidation. Proc Natl Acad Sci USA. 2005;102:6478–83.Article 
CAS 

Google Scholar 
Johnson KS, Beehler CL, Sakamoto-Arnold CM, Childress JJ. In situ measurements of chemical distributions in a deep-sea hydrothermal vent field. Science. 1986;231:1139–41.Article 
CAS 

Google Scholar 
Childress JJ, Girguis PR. The metabolic demands of endosymbiotic chemoautotrophic metabolism on host physiological capacities. J Exp Biol. 2011;214:312–25.Article 
CAS 

Google Scholar 
Hentschel U, Hand S, Felbeck H. The contribution of nitrate respiration to the energy budget of the symbiont-containing clam Lucinoma aequizonata: a calorimetric study. J Exp Biol. 1996;199:427–33.Article 
CAS 

Google Scholar 
Breusing C, Mitchell J, Delaney J, Sylva SP, Seewald JS, Girguis PR, et al. Physiological dynamics of chemosynthetic symbionts in hydrothermal vent snails. ISME J. 2020;14:2568–79.Article 
CAS 

Google Scholar 
Amorim K, Loick-Wilde N, Yuen B, Osvatic JT, Wäge-Recchioni J, Hausmann B, et al. Chemoautotrophy, symbiosis and sedimented diatoms support high biomass of benthic molluscs in the Namibian shelf. Sci Rep. 2022;12:9731.Article 
CAS 

Google Scholar 
Breusing C, Johnson SB, Tunnicliffe V, Clague DA, Vrijenhoek RC, Beinart RA. Allopatric and sympatric drivers of speciation in Alviniconcha hydrothermal vent snails. Mol Biol Evol. 2020;37:3469–84.Article 
CAS 

Google Scholar 
Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.Article 

Google Scholar 
Brissac T, Gros O, Merçot H. Lack of endosymbiont release by two Lucinidae (Bivalvia) of the genus Codakia: consequences for symbiotic relationships. FEMS Microbiol Ecol. 2009;67:261–7.Article 
CAS 

Google Scholar 
Werner GDA, Cornelissen JHC, Cornwell WK, Soudzilovskaia NA, Kattge J, West SA, et al. Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown. Proc Natl Acad Sci USA. 2018;115:5229–34.Article 
CAS 

Google Scholar 
Sudakaran S, Kost C, Kaltenpoth M. Symbiont acquisition and replacement as a source of ecological innovation. Trends Microbiol. 2017;25:375–90.Article 
CAS 

Google Scholar 
Li Y, Liles MR, Halanych KM. Endosymbiont genomes yield clues of tubeworm success. ISME J. 2018;12:2785–95.Article 
CAS 

Google Scholar 
Moran NA, Yun Y. Experimental replacement of an obligate insect symbiont. Proc Natl Acad Sci USA. 2015;112:2093–6.Article 
CAS 

Google Scholar 
Sørensen MES, Wood AJ, Cameron DD, Brockhurst MA. Rapid compensatory evolution can rescue low fitness symbioses following partner switching. Curr Biol. 2021;31:3721–3728.e4.Article 

Google Scholar 
Taylor JD, Glover EA, Smith L, Ikebe C, Williams ST. New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia). Zootaxa. 2016;4196:zootaxa.4196.3.2.Article 

Google Scholar 
Osvatic J. Fig1 gtdb tree and alignment. figshare. 2021. https://doi.org/10.6084/m9.figshare.16837216.v1.Osvatic J. Figure 2: GTDB alignment and phylogeny. 2021. https://doi.org/10.6084/m9.figshare.16837237. More

Alphan, H. Land use change and urbanisation of Adana, Turkey. Land Degrad. Dev. 14, 575–586 (2003).Article 

Google Scholar 
Catalàn, B., Sauri, D. & Serra, P. Urban sprawl in the Mediterranean? Patterns of growth and change in the Barcelona Metropolitan Region 1993–2000. Landsc. Urban Plan. 85(3–4), 174–184 (2008).
Google Scholar 
Chen, K., Long, H., Liao, L., Tu, S. & Li, T. Land use transitions and urban-rural integrated development: Theoretical framework and China’s evidence. Land Use Policy 92, 104465 (2020).Article 

Google Scholar 
Bianchini, L. et al. Forest transition and metropolitan transformations in developed countries: Interpreting apparent and latent dynamics with local regression models. Land 11(1), 12 (2021).Article 

Google Scholar 
Angel, S., Parent, J., Civco, D. L., Blei, A. & Potere, D. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plan. 75(2), 53–107 (2011).Article 

Google Scholar 
Fischer, A. P. Forest landscapes as social-ecological systems and implications for management. Landsc. Urban Plan. 177, 138–147 (2018).Article 

Google Scholar 
Darvishi, A., Yousefi, M. & Marull, J. Modelling landscape ecological assessments of land use and cover change scenarios. Application to the Bojnourd Metropolitan Area (NE Iran). Land Use Policy 99, 105098 (2020).Article 

Google Scholar 
Cheng, L. L., Tian, C. & Yin, T. T. Identifying driving factors of urban land expansion using Google earth engine and machine-learning approaches in Mentougou District, China. Sci. Rep. 12(1), 1–13 (2022).Article 
CAS 

Google Scholar 
Kasanko, M. et al. Are European Cities becoming dispersed? A comparative analysis of fifteen European urban areas. Landsc. Urban Plan. 77(1–2), 111–130 (2006).Article 

Google Scholar 
Terzi, F. & Bolen, F. Urban sprawl measurement of Istanbul. Eur. Plan. Stud. 17(10), 1559–1570 (2009).Article 

Google Scholar 
Angel, S., Parent, J. & Civco, D. L. Ten compactness properties of circles: measuring shape in geography. Can. Geogr. 54, 441–461 (2010).Article 

Google Scholar 
Salvati, L., Gemmiti, R. & Perini, L. Land degradation in Mediterranean urban areas: An unexplored link with planning?. Area 44(3), 317–325 (2012).Article 

Google Scholar 
Attorre, F., Bruno, M., Francesconi, F., Valenti, R. & Bruno, F. Landscape changes of Rome through tree-lined roads. Landsc. Urban Plan. 49, 115–128 (2000).Article 

Google Scholar 
Turok, I. & Mykhnenko, V. The trajectories of European cities, 1960–2005. Cities 24(3), 165–182 (2007).Article 

Google Scholar 
Ioannidis, C., Psaltis, C. & Potsiou, C. Towards a strategy for control of suburban informal buildings through automatic change detection. Comput. Environ. Urban Syst. 33, 64–74 (2009).Article 

Google Scholar 
Grekousis, G., Manetos, P. & Photis, Y. N. Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the athens metropolitan area. Cities 30, 193–203 (2013).Article 

Google Scholar 
Salvati, L. Towards a polycentric region? The socioeconomic trajectory of Rome, an ‘Eternally Mediterranean’ city. Tijdschr. Econ. Soc. Geogr. 105(3), 268–284 (2014).Article 

Google Scholar 
Chorianopoulos, I., Pagonis, T., Koukoulas, S. & Drymoniti, S. Planning, competitiveness and sprawl in the Mediterranean city: The case of Athens. Cities 27, 249–259 (2010).Article 

Google Scholar 
Munafò, M., Salvati, L. & Zitti, M. Estimating soil sealing rate at national level—Italy as a case study. Ecol. Ind. 26, 137–140 (2013).Article 

Google Scholar 
Morelli, V. G., Rontos, K. & Salvati, L. Between suburbanisation and re-urbanisation: Revisiting the urban life cycle in a Mediterranean compact city. Urban Res. Pract. 7(1), 74–88 (2014).Article 

Google Scholar 
Basem Ajjur, S. & Al-Ghamdi, S. G. Exploring urban growth–climate change–flood risk nexus in fast growing cities. Sci. Rep. 12, 12265 (2022).Article 
ADS 

Google Scholar 
Li, H. & Wu, J. Use and misuse of landscape indices. Landsc. Ecol. 19, 389–399 (2004).Article 

Google Scholar 
Salvati, L. Agro-forest landscape and the ‘fringe’city: A multivariate assessment of land-use changes in a sprawling region and implications for planning. Sci. Total Environ. 490, 715–723 (2014).Article 
ADS 
CAS 

Google Scholar 
Sang, X. et al. Intensity and stationarity analysis of land use change based on CART algorithm. Sci. Rep. 9(1), 1–12 (2019).Article 
ADS 
CAS 

Google Scholar 
Ettehadi Osgouei, P., Sertel, E. & Kabadayı, M. E. Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020. Sci. Rep. 12(1), 1–17 (2022).Article 

Google Scholar 
He, S., Yu, S., Li, G. & Zhang, J. Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities. Land Use Policy 95, 104576 (2020).Article 

Google Scholar 
Bockarjova, M., Wouter Botzen, W. J., Bulkeley, H. A. & Toxopeus, H. Estimating the social value of nature-based solutions in European cities. Sci. Rep. 12, 19833 (2022).Article 
ADS 
CAS 

Google Scholar 
Liu, J. & Niyogi, D. Meta-analysis of urbanisation impact on rainfall modification. Sci. Rep. 9(1), 1–14 (2019).ADS 

Google Scholar 
Holland, J. H. Studying complex adaptive systems. J. Syst. Sci. Complex. 19(1), 1–8 (2006).Article 
MathSciNet 
MATH 

Google Scholar 
Salvati, L. & Serra, P. Estimating rapidity of change in complex urban systems: A multidimensional, local-scale approach. Geogr. Anal. 48(2), 132–156 (2016).Article 

Google Scholar 
Bura, S., Guerin-Pace, F., Mathian, H., Pumain, D. & Sanders, L. Multi-agents systems and the dynamics of a settlement system. Geogr. Anal. 28(2), 161–178 (1996).Article 

Google Scholar 
Hasse, J. E. & Lathrop, R. G. Land resource impact indicators of urban sprawl. Appl. Geogr. 23, 159–175 (2003).Article 

Google Scholar 
Grafius, D. R., Corstanje, R. & Harris, J. A. Linking ecosystem services, urban form and green space configuration using multivariate landscape metric analysis. Landsc. Ecol. 33(4), 557–573 (2018).Article 

Google Scholar 
Pumain, D. Hierarchy in Natural and Social Sciences (Kluwer-Springer, 2005).
Google Scholar 
Cabral, P., Augusto, G., Tewolde, M. & Araya, Y. Entropy in urban systems. Entropy 15(12), 5223–5236 (2013).Article 
ADS 

Google Scholar 
Salvati, L. & Carlucci, M. In-between stability and subtle changes: Urban growth, population structure, and the city life cycle in Rome. Popul. Space Place 22(3), 216–227 (2016).Article 

Google Scholar 
Batty, M. & Longley, P. Fractal Cities (Academic Press, 1994).MATH 

Google Scholar 
Berry, B. J. L. Cities as systems within systems of cities. Pap. Reg. Sci. 13, 147–163 (2005).Article 

Google Scholar 
Petrosillo, I. et al. The resilient recurrent behavior of mediterranean semi-arid complex adaptive landscapes. Land 10(3), 296 (2021).Article 

Google Scholar 
Portugali, J. Complexity, Cognition and the City, Understanding Complex Systems (Springer, 2011).Book 

Google Scholar 
Wu, J., Jenerette, G. D., Buyantuyev, A. & Redman, C. L. Quantifying spatiotemporal patterns of urbanisation: The case of the two fastest growing metropolitan regions in the United States. Ecol. Complex. 8(1), 1–8 (2011).Article 

Google Scholar 
Sun, Y., Gao, C., Li, J., Li, W. & Ma, R. Examining urban thermal environment dynamics and relations to biophysical composition and configuration and socioeconomic factors: A case study of the Shanghai metropolitan region. Sustain. Cities Soc. 40, 284–295 (2018).Article 

Google Scholar 
Phillips, M. A. & Ritala, P. A complex adaptive systems agenda for ecosystem research methodology. Technol. Forecast. Soc. Change 148, 119739 (2019).Article 

Google Scholar 
Walker, B., Holling, C. S., Carpenter, S. R. & Kinzig, A. Resilience, adaptability and transformability in social-ecological systems. Ecol. Soc. 9(2), 5 (2004).Article 

Google Scholar 
Kelly, C. et al. Community resilience and land degradation in forest and shrublandsocio-ecological systems: A case study in Gorgoglione, Basilicata regionn, Italy. Land Use Policy 46, 11–20 (2015).Article 

Google Scholar 
Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems. Ecol. Soc. 23(4), 46 (2018).Article 

Google Scholar 
Ferrara, A. et al. Shaping the role of ‘fast’ and ‘slow’ drivers of change in forest-shrubland socio-ecological systems. J. Environ. Manag. 169, 155–166 (2016).Article 

Google Scholar 
Lamy, T., Liss, K. N., Gonzalez, A. & Bennett, E. M. Landscape structure affects the provision of multiple ecosystem services. Environ. Res. Lett. 11(12), 124017 (2016).Article 
ADS 

Google Scholar 
Riitters, K. H., Vogt, P., Soille, P., Kozak, J. & Estreguil, C. Neutral model analysis of landscape patterns from mathematical morphology. Landsc. Ecol. 22(7), 1033–1043 (2007).Article 

Google Scholar 
Riitters, K., Vogt, P., Soille, P. & Estreguil, C. Landscape patterns from mathematical morphology on maps with contagion. Landsc. Ecol. 24(5), 699–709 (2009).Article 

Google Scholar 
Anas, A., Arnott, R. & Small, K. Urban spatial structure. J. Econ. Lit. 36(3), 1426–1464 (1998).
Google Scholar 
Arroyo-Mora, J. P., Sánchez-Azofeifa, G. A., Rivard, B., Calvo, J. C. & Janzen, D. H. Dynamics in landscape structure and composition for the Chorotega region, Costa Rica from 1960 to 2000. Agr. Ecosyst. Environ. 106(1), 27–39 (2005).Article 

Google Scholar 
Siles, G., Charland, A., Voirin, Y. & Bénié, G. B. Integration of landscape and structure indicators into a web-based geoinformation system for assessing wetlands status. Eco. Inform. 52, 166–176 (2019).Article 

Google Scholar 
Soille, P. Morphological Image Analysis: Principles and Applications (Springer, 2003).MATH 

Google Scholar 
Soille, P. & Vogt, P. Morphological segmentation of binary patterns. Pattern Recogn. Lett. 30, 456–459 (2009).Article 
ADS 

Google Scholar 
Vogt, P. et al. Mapping spatial patterns with morphological image processing. Landsc. Ecol. 22(2), 171–177 (2007).Article 

Google Scholar 
Bajocco, S., Ceccarelli, T., Smiraglia, D., Salvati, L. & Ricotta, C. Modeling the ecological niche of long-term land use changes: The role of biophysical factors. Ecol. Ind. 60, 231–236 (2016).Article 

Google Scholar 
Yin, Y., Zhou, K. & Chen, Y. Deconstructing the driving factors of land development intensity from multi-scale in differentiated functional zones. Sci. Rep. 12(1), 1–13 (2022).Article 

Google Scholar 
Duvernoy, I., Zambon, I., Sateriano, A. & Salvati, L. Pictures from the other side of the fringe: Urban growth and peri-urban agriculture in a post-industrial city (Toulouse, France). J. Rural. Stud. 57, 25–35 (2018).Article 

Google Scholar 
Smiraglia, D., Ceccarelli, T., Bajocco, S., Salvati, L. & Perini, L. Linking trajectories of land change, land degradation processes and ecosystem services. Environ. Res. 147, 590–600 (2016).Article 
CAS 

Google Scholar 
Shaker, R. R. Examining sustainable landscape function across the Republic of Moldova. Habitat Int. 72, 77–91 (2018).Article 
ADS 

Google Scholar 
Zheng, H. & Li, H. Spatial–temporal evolution characteristics of land use and habitat quality in Shandong Province, China. Sci. Rep. 12(1), 1–12 (2022).Article 

Google Scholar 
Tombolini, I., Munafò, M. & Salvati, L. Soil sealing footprint as an indicator of dispersed urban growth: A multivariate statistics approach. Urban Res. Pract. 9(1), 1–15 (2016).Article 

Google Scholar 
Salvati, L., Sateriano, A., Grigoriadis, E. & Carlucci, M. New wine in old bottles: The (changing) socioeconomic attributes of sprawl during building boom and stagnation. Ecol. Econ. 131, 361–372 (2017).Article 

Google Scholar 
Zambon, I., Benedetti, A., Ferrara, C. & Salvati, L. Soil matters? A multivariate analysis of socioeconomic constraints to urban expansion in Mediterranean Europe. Ecol. Econ. 146, 173–183 (2018).Article 

Google Scholar 
Paul, V. & Tonts, M. Containing urban sprawl: Trends in land use and spatial planning in the Metropolitan Region of Barcelona. J. Environ. Plann. Manag. 48(1), 7–35 (2005).Article 

Google Scholar 
Serra, P., Vera, A., Tulla, A. F. & Salvati, L. Beyond urban–rural dichotomy: Exploring socioeconomic and land-use processes of change in Spain (1991–2011). Appl. Geogr. 55, 71–81 (2014).Article 

Google Scholar 
Seifollahi-Aghmiuni, S., Kalantari, Z., Egidi, G., Gaburova, L. & Salvati, L. Urbanisation-driven land degradation and socioeconomic challenges in peri-urban areas: Insights from Southern Europe. Ambio 51(6), 1446–1458 (2022).Article 

Google Scholar 
Pili, S., Grigoriadis, E., Carlucci, M., Clemente, M. & Salvati, L. Towards sustainable growth? A multi-criteria assessment of (changing) urban forms. Ecol. Ind. 76, 71–80 (2017).Article 

Google Scholar 
Salvati, L., Sateriano, A. & Grigoriadis, E. Crisis and the city: Profiling urban growth under economic expansion and stagnation. Lett. Spat. Resour. Sci. 9(3), 329–342 (2016).Article 

Google Scholar 
Champion, T. & Hugo, G. New Forms of Urbanisation: Beyond the Urban-Rural Dichotomy (Ashgate, 2004).
Google Scholar 
Frondoni, R., Mollo, B. & Capotorti, G. A landscape analysis of land cover change in the municipality of Rome (Italy): Spatio-temporal characteristics and ecological implications of land cover transitions from 1954 to 2001. Landsc. Urban Plan. 100(1–2), 117–128 (2011).Article 

Google Scholar 
Perrin, C., Nougarèdes, B., Sini, L., Branduini, P. & Salvati, L. Governance changes in peri-urban farmland protection following decentralisation: A comparison between Montpellier (France) and Rome (Italy). Land Use Policy 70, 535–546 (2018).Article 

Google Scholar 
Salvati, L. Monitoring high-quality soil consumption driven by urban pressure in a growing city (Rome, Italy). Cities 31, 349–356 (2013).Article 

Google Scholar 
Salvati, L., Ciommi, M. T., Serra, P. & Chelli, F. M. Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy. Land Use Policy 81, 143–152 (2019).Article 

Google Scholar 
Ferrara, C., Salvati, L. & Tombolini, I. An integrated evaluation of soil resource depletion from diachronic settlement maps and soil cartography in peri-urban Rome, Italy. Geoderma 232, 394–405 (2014).Article 
ADS 

Google Scholar 
Egidi, G. & Salvati, L. Beyond the suburban-urban divide: Convergence in age structures in metropolitan Rome, Italy. J. Popul. Soc. Stud. 28(2), 130–142 (2020).Article 

Google Scholar 
Pili, S., Serra, P. & Salvati, L. Landscape and the city: Agro-forest systems, land fragmentation and the ecological network in Rome, Italy. Urban For. Urban Green. 41, 230–237 (2019).Article 

Google Scholar 
European Environment Agency. Urban Sprawl in Europe – The Ignored Challenge. Copenhagen: EEA Report no. 10 (2006).Park, S., Hepcan, Ç. C., Hepcan, Ş & Cook, E. A. Influence of urban form on landscape pattern and connectivity in metropolitan regions: a comparative case study of Phoenix, AZ, USA, and Izmir, Turkey. Environ. Monit. Assess. 186(10), 6301–6318 (2014).Article 

Google Scholar 
Luo, F., Liu, Y., Peng, J. & Wu, J. Assessing urban landscape ecological risk through an adaptive cycle framework. Landsc. Urban Plan. 180, 125–134 (2018).Article 

Google Scholar 
Ortega, M., Pascual, S., Elena-Rosselló, R. & Rescia, A. J. Land-use and spatial resilience changes in the Spanish olive socio-ecological landscape. Appl. Geogr. 117, 102171 (2020).Article 

Google Scholar 
Parcerisas, L. et al. Land use changes, landscape ecology and their socioeconomic driving forces in the Spanish Mediterranean coast (El Maresme County, 1850–2005). Environ. Sci. Policy 23, 120–132 (2012).Article 

Google Scholar 
Masini, E. et al. Urban growth, land-use efficiency and local socioeconomic context: A comparative analysis of 417 metropolitan regions in Europe. Environ. Manag. 63(3), 322–337 (2019).Article 
ADS 

Google Scholar 
Luck, M. & Wu, J. A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landsc. Ecol. 17(4), 327–339 (2002).Article 

Google Scholar 
Pesaresi, M. & Bianchin, A. Recognising settlement structure using mathematical morphology and image texture. Remote Sensing Urban Anal. GISDATA 9, 46–60 (2003).
Google Scholar 
Schneider, A. & Woodcock, C. E. Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Stud. 45(3), 659–692 (2008).Article 

Google Scholar 
Mubareka, S., Koomen, E., Estreguil, C. & Lavalle, C. Development of a composite index of urban compactness for land use modelling applications. Landsc. Urban Plan. 103(3–4), 303–317 (2011).Article 

Google Scholar 
Vogt, P. et al. Mapping landscape corridors. Ecol. Ind. 7(2), 481–488 (2007).Article 

Google Scholar 
Daya Sagar, B. S. & Murthy, K. S. R. Generation of a fractal landscape using nonlinear mathematical morphological transformations. Fractals 8(03), 267–272 (2000).Article 

Google Scholar 
Scott, A. J., Carter, C., Reed, M. R., Stonyer, B. & Coles, R. Disintegrated development at the rural-urban fringe: Re-connecting spatial planning theory and practice. Prog. Plan. 83, 1–52 (2013).Article 

Google Scholar 
Zhao, Q., Wen, Z., Chen, S., Ding, S. & Zhang, M. Quantifying land use/land cover and landscape pattern changes and impacts on ecosystem services. Int. J. Environ. Res. Public Health 17(1), 126 (2020).Article 

Google Scholar 
Parr, J. The regional economy, spatial structure and regional urban systems. Reg. Stud. 48(12), 1926–1938 (2014).Article 

Google Scholar 
Salvati, L., Zambon, I., Chelli, F. M. & Serra, P. Do spatial patterns of urbanisation and land consumption reflect different socioeconomic contexts in Europe?. Sci. Total Environ. 625, 722–730 (2018).Article 
ADS 
CAS 

Google Scholar 
Coppi, R. & Bolasco, S. Multiway Data Analysis (Elsevier, 1988).MATH 

Google Scholar 
Kroonenberg, P. M. Applied Multiway Data Analysis (Wiley, 2008).Book 
MATH 

Google Scholar 
Escofier, B. & Pages, J. Multiple factor analysis (AFMULT Package). Comput. Stat. Data Anal. 18, 121–140 (1994).Article 
MATH 

Google Scholar 
De Rosa, S. & Salvati, L. Beyond a ‘side street story’? Naples from spontaneous centrality to entropic polycentricism, towards a ‘crisis city’. Cities 51, 74–83 (2016).Article 

Google Scholar 
Favaro, J.-M. & Pumain, D. Gibrat revisited: An urban growth model incorporating spatial interaction and innovation cycles. Geogr. Anal. 43(3), 261–286 (2011).Article 

Google Scholar 
Walker, B. H., Carpenter, S. R., Rockstrom, J., Crepin, A.-S. & Peterson, G. D. “Drivers, “slow” variables, “fast” variables, shocks, and resilience. Ecol. Soc. 17(3), 30 (2012).Article 

Google Scholar 
Zhang, Z., Su, S., Xiao, R., Jiang, D. & Wu, J. Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay, China. Appl. Geogr. 45, 193–202 (2013).Article 

Google Scholar 
Fratarcangeli, C., Fanelli, G., Franceschini, S., De Sanctis, M. & Travaglini, A. Beyond the urban-rural gradient: Self-organising map detects the nine landscape types of the city of Rome. Urban For. Urban Green. 38, 354–370 (2019).Article 

Google Scholar 
Crisci, M., Benassi, F., Rabiei-Dastjerdi, H., McArdle, G. Spatio-temporal variations and contextual factors of the supply of Airbnb in Rome. An initial investigation. Lett. Spat. Resour. Sci. 1–17 (2022).Lelo, K., Monni, S. & Tomassi, F. Socio-spatial inequalities and urban transformation. The case of Rome districts. Socio-Econ. Plann. Sci. 68, 100696 (2019).Article 

Google Scholar 
Crisci, M. The impact of the real estate crisis on a south european metropolis: From urban diffusion to Reurbanisation. Appl. Spat. Anal. Policy 15(3), 797–820 (2022).Article 

Google Scholar 
Wang, Y. & Zhang, X. A dynamic modeling approach to simulating socioeconomic effects on landscape changes. Ecol. Model. 140(1–2), 141–162 (2001).Article 

Google Scholar 
Voghera, A. The River agreement in Italy. Resilient planning for the co-evolution of communities and landscapes. Land Use Policy 91, 104377 (2020).Article 

Google Scholar 
Chen, A. & Partridge, M. D. When are cities engines of growth in China? Spread and backwash effects across the urban hierarchy. Reg. Stud. 47(8), 1313–1331 (2013).Article 

Google Scholar 
Ciommi, M., Chelli, F. M., Carlucci, M. & Salvati, L. Urban growth and demographic dynamics in southern Europe: Toward a new statistical approach to regional science. Sustainability 10(8), 2765 (2018).Article 

Google Scholar 
Jacobs-Crisioni, C., Rietveld, P. & Koomen, E. The impact of spatial aggregation on urban development analyses. Appl. Geogr. 47, 46–56 (2014).Article 

Google Scholar 
Kourtit, K., Nijkamp, P. & Reid, N. The new urban world: Challenges and policy. Appl. Geogr. 49, 1–3 (2014).Article 

Google Scholar 
Bruegmann, R. Sprawl: A Compact History (University of Chicago Press, 2005).Book 

Google Scholar 
Neuman, M. & Hull, A. The Futures of the City Region. Reg. Stud. 43(6), 777–787 (2009).Article 

Google Scholar 
Couch, C., Petschel-held, G. & Leontidou, L. Urban Sprawl In Europe: Landscapes, Land-use Change and Policy (Blackwell, 2007).Book 

Google Scholar 
Longhi, C. & Musolesi, A. European cities in the process of economic integration: towards structural convergence. Ann. Reg. Sci. 41, 333–351 (2007).Article 

Google Scholar 
Tian, G., Ouyang, Y., Quan, Q. & Wu, J. Simulating spatiotemporal dynamics of urbanisation with multi-agent systems—A case study of the Phoenix metropolitan region, USA. Ecol. Model. 222(5), 1129–1138 (2011).Article 

Google Scholar 
Tian, L., Chen, J. & Yu, S. X. Coupled dynamics of urban landscape pattern and socioeconomic drivers in Shenzhen, China. Landsc. Ecol. 29(4), 715–727 (2014).Article 

Google Scholar 
Fielding, A. J. Counterurbanization in Western Europe. Prog. Plan. 17, 1–52 (1982).Article 

Google Scholar 
Oueslati, W., Alvanides, S. & Garrod, G. Determinants of urban sprawl in European cities. Urban Stud. 52(9), 1594–1614 (2015).Article 

Google Scholar 
Tress, B., Tress, G., Décamps, H. & d’Hauteserre, A. M. Bridging human and natural sciences in landscape research. Landsc. Urban Plan. 57(3–4), 137–141 (2001).Article 

Google Scholar 
Xu, Z., Lv, Z., Li, J., Sun, H. & Sheng, Z. A Novel perspective on travel demand prediction considering natural environmental and socioeconomic factors. IEEE Intell. Transp. Syst. Mag. https://doi.org/10.1109/MITS.2022.3162901 (2022).Article 

Google Scholar 
Xu, Z., Lv, Z., Li, J. & Shi, A. A novel approach for predicting water demand with complex patterns based on ensemble learning. Water Resour. Manag. 36(11), 4293–4312 (2022).Article 

Google Scholar 
Lv, Z., Li, J., Dong, C., Li, H. & Xu, Z. Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalisation index. Data Knowl. Eng. 135, 101912 (2021).Article 

Google Scholar  More

Microbial community composition in cave sedimentsThe ITS1 dataset generated 5,793,980 raw sequence reads, resulting in 5,458,895 gene quality-filtered reads, ranging from 1252 up to 540,803 per sample. After singletons and rare taxa ( More

Conservationist Jim Groombridge in Hawaii (standing) performing a ‘heli-hook-up’, in which a net full of equipment is hooked up to the hovering helicopter, to save it needing to land.Credit: Jim Groombridge/Maui Forest Bird Recovery Project

Since his undergraduate degree, Jim Groombridge has been part of several teams that work with critically endangered animals, including the Mauritius kestrel (Falco punctatus), which was brought back from the brink of extinction. But he has also experienced the devastation of some species being lost forever, despite all possible interventions. After receiving his PhD from Queen Mary University of London in 2000, he worked as a project coordinator at the Maui Forest Bird Recovery Project in Makawao, Hawaii. Conservation science spans many topics including climate change, working with local communities, epidemiology, genomics and designing protected areas. Projects can range from single-species conservation to ecosystem-level or landscape conservation, such as restoring whole islands. Now a professor in biodiversity conservation at the University of Kent’s Durrell Institute of Conservation and Ecology in Canterbury, UK, Groombridge teaches bachelor’s and master’s students about leadership of conservation teams and how to motivate them in the face of setbacks.What is special about leading conservation teams?Conservation field teams are slightly quirky, and those quirks can define what makes a team work well or not. One is that team leaders are rarely trained in management tasks, such as overseeing a budget, interacting with project partners and local governments, dealing with team members who feel passionate about what they do and facing the high stakes involved. Team members are enthusiastic, passionate and seldom motivated by money.Another quirk is that, in a small conservation team of four to six people, there is often a mix of skill sets and experience. You can have highly experienced specialists in a particular area, such as screening parrots for diseases, or reintroduction biology, and you might also have volunteers with only passion and enthusiasm to offer.How do you lead a team with such variable experience?Even with those different levels of expertise, you still need to meet high standards for specimen and data collection. At the moment, for example, I’m sequencing the genome of the pink pigeon (Nesoenas mayeri), using samples collected in the 1990s. There’s a sense of responsibility, especially if you’re working with species that are rare, because if you mess it up, they could go extinct. It’s not unusual to have volunteers with only two or three weeks’ worth of experience handling extremely rare samples or working with valuable data sets. Their learning curve is pretty steep. As a leader, you need to make sure that you understand the details — ranging from tasks such as collecting data and monitoring and recording invasive species to, for example, knowing how to trap a mongoose — so that you can make sure that everyone is collecting the data in the same way.

Jim Groombridge (far left), who studies biodiversity conservation at the University of Kent, UK, with one of the field crews involved in an operation to translocate a bird called the po‘ouli in Hawaii.Credit: Jim Groombridge/Maui Forest Bird Recovery Project

What do team members tend to have in common?They often share a passion for nature. They want to save the environment, they want to save a species from going extinct, they want to make a difference. That level of emotion is important. It creates an energy, which needs to be channelled proactively and positively into the project to make it a success.In 2002, for example, I was leading a team working to save a bird called the po‘ouli (Melamprosops phaeosoma) on the island of Maui, part of the Hawaiian archipelago. We were trying to translocate one of the last known birds into the range of another one to give them the opportunity to breed. There was huge excitement, but after four weeks of failing to catch the bird, there was also a lot of frustration.How do you manage a team with such strong emotions?Morale is really important. So is being able to deal with difficulties when they arise. That’s what gets small teams through tough times. With the po‘ouli, I had to make sure that the team had fun, and that people genuinely enjoyed themselves. That meant taking time out with the team in the evenings and ensuring that everyone had a bit of a laugh, so it wasn’t deadly serious all the time. Also, I made sure that team members got to perform the aspects of the job that they were good at, to increase their confidence and well-being. We eventually trapped the po‘ouli and moved it, but even though the birds were in the same territory, they didn’t breed.How do you manage expectations amid failure?I had to remind the team about the broader picture of what we had achieved. This was the first time anyone had followed the po‘ouli in the forest for ten days. I think we learnt more about the ecology of that species in that time than anyone had learnt in 30 years. We held the translocated bird for about two hours before we released it, and it took food items from us, which showed that the birds could be kept in captivity if necessary. We learnt a huge amount that could be applied to another project.
Treading carefully: saving frankincense trees in Yemen
You have to manage people’s expectations and have goals that are achievable. If you are starting a project on a species with fewer than ten individuals left in the wild, and your goal is to have thousands, that’s a difficult leap of imagination. Instead, perhaps start with finding a food that a species would eat in captivity. People need to remain connected with what’s achievable. There’s a delicate balance between being aspirational and being pragmatic.As a team member, what do you wish more conservation leaders knew?Often, there is too much emphasis placed on the command structure. Innovation in a conservation team is undersold, and easily quashed by a type of line-manager approach. The hierarchy in a team is important because people know what to do and who to report to, but you also have to encourage team members to use their initiative and ask questions. I remember when my team and I were in the cloud forests, tropical mountain regions covered by clouds for most of the year in Hawaii, we were struggling with baiting rats, which prey on eggs and fledglings of native birds. It’s one of the wettest places on Earth, and the rat poison basically turns to cottage cheese. However, one of my colleagues designed a bait box, which kept the bait dry for many weeks. When you’re working with critically endangered species and in field conditions, ingenuity is crucial.
This interview has been edited for length and clarity. More

Sequence analysisProtein sequence searches were conducted in the GenBank nr database with BLASTP42 using default parameters, including automatically adjusting parameters for short input sequences (Table S1). Conserved domain searches were run against the GenBank Conserved Domain Database (CDD)43. Sequence alignments were conducted in CLC Main Workbench 20.0.4 (Qiagen) with the fast alignment algorithm, gap open cost = 10, and gap extension cost = 1. Biochemical properties of the fish putative thiaminase I protein sequences were predicted with the Create Sequence Statistics function in CLC Main Workbench 20.0.4 (Qiagen, Hilden, Germany). The molecular weights were calculated from the sum of the amino acids in the sequence, and the isoelectric points (pIs) were calculated from the pKa values for the individual amino acids in the sequence.Bacteria culturePure cultures of P. thiaminolyticus strain 818822 were cultured at 37 °C in Terrific Broth (MO BIO Laboratories, Carlsbad, CA) in either a shaking incubator or in a beveled flask with a stir bar and were harvested after 48–80 h of culture. Upon harvest, cultures were processed immediately or frozen whole in 50 mL Falcon tubes at − 80 °C. Fresh or thawed cultures were spun at 14,000×g, and culture supernatant was concentrated using Amicon-ultra 10 kDa molecular weight cut-off (MWCO) filters (EMD Millipore, Billerica, MA).The zebrafish and alewife candidate thiaminase I genes were cloned and overexpressed in E. coli to determine whether they produced functional thiaminases. The recombinant thiaminase I gene from P. thiaminolyticus was overexpressed in E. coli as a positive control. Candidate and control genes were synthesized (Integrated DNA Technologies, Inc., Coralville, Iowa) and placed into the pET52b vector (EMD Millipore). Insert sequences are provided in Supplementary Figs. S10–S13. The empty pET52b vector was used as a negative control. The plasmid was transformed into E. coli (Rosetta 2(DE3)pLysS Singles Competent Cells, EMD Millipore) according to the manufacturer’s instructions, and expression of candidate genes was induced by the addition of IPTG. Cells were lysed in 1X BugBuster (Millipore) according to the manufacturer’s instructions in the presence of benzonase nuclease, and soluble and insoluble fractions were separated by centrifugation.Tissue collectionsAdult common carp were captured from Lake Erie using short-set gill nets. Adult alewife and quagga mussels (Dreissena bugensis) were collected from Sturgeon Bay, Lake Michigan using bottom trawls. Fish collections were completed during July 2007. Sex of sampled fish was not identified. Upon collection, unanesthetized animals were immediately euthanized by flash freezing between slabs of dry ice and stored at − 80 °C. Fish were harvested by the Great Lakes Science Center, U.S. Geological Survey (USGS). Laboratory use of frozen animal tissues and wild type and recombinant bacteria was in accordance with institutional guidelines and biosafety procedures at Oregon State University and USGS. Animal care and use procedures were approved by the Great Lakes Science Center, USGS. All USGS sampling and handling of fish during research are carried out in accordance with guidelines for the care and use of fishes by the American Fisheries Society44. All methods are reported in accordance with applicable ARRIVE guidelines (https://arriveguidelines.org). Zebrafish from OSU’s zebrafish facility were anesthetized and euthanized by overdose with waterborne 200 ppm ethyl 3-aminobenzoate methanesulfonate (MS-222, Sigma-Aldrich, St. Louis, MO) following protocols approved by the OSU Animal Institutional Care and Use Committee and were frozen at − 80 °C after euthanization. Gills, liver, spleen, and the intestinal tract were dissected, and gill tissue was homogenized separately from liver, spleen, and gut, which were homogenized together and designated “viscera.” Homogenization and protein preparation procedures were the same as that for alewife. Zebrafish from Columbia Environmental Research Center (CERC), USGS cultures were anesthetized and euthanized by overdose with 200 ppm ethyl 3-aminobenzoate methanesulfonate (MS-222, Sigma-Aldrich, St. Louis, MO) in water following protocols approved by CERC Institutional Animal Care and Use Committee (IACUC). Whole fish (0.2–0.6 g) were homogenized in 10 mL cold phosphate buffer, pH 6.5. Whole common carp and alewife were thawed until they could just be dissected. Preliminary trial extractions on alewife stomach and intestines, spleen, and gills revealed similar results and revealed that gills and spleen tissue produced the cleanest protein preparations. Therefore, subsequent extractions for common carp and alewife used gill tissue. Samples were pooled from 3 to 5 individual fish, haphazardly chosen from the sampled fish without exclusions. Quagga mussels were thawed just sufficiently to be husked from their shell and were used whole. Researchers were aware of the species and tissue designation of each sample throughout the experiments. Animal tissues were placed in ice-cold (4 °C) beakers containing cold extraction buffer (16 mM K3HPO4, 84 mM KH2PO4, 100 mM NaCl, pH 6.5 with 1 mM DTT, 2 mM EDTA, 3 mM Pepstatin, 1X Protease inhibitor cocktail (Sigma), and 1 mM AEBSF). All extractions were carried out at 4 °C in pre-chilled glassware. Samples were mechanically homogenized using a rotor–stator tissue grinder. Samples were stirred gently for several hours to overnight at 4 °C, centrifuged at 14,000×g to remove debris, and strained through cheesecloth to remove any insoluble lipids. Extracts were then subjected to 30–75% ammonium sulfate precipitation. Pellets from the precipitation were resuspended in buffer (83 mM KH2PO4, 17 mM K2HPO4, and 100 mM NaCl), centrifuged to remove any remaining debris, and stored in 30% glycerol at − 20 °C.Protein electrophoresisNative PAGE was run using either pre-cast TGX gels (BioRad, Hercules, California) of varying percentage (7.5% to 12% or 8–16% gradient gels) or on hand-cast gels (TGX FastCast, BioRad) made according to the manufacturer’s instructions.Blue-native PAGE was used to estimate the mass of thiaminases in their native conformation. Blue-native PAGE45 gels were run using the NativePage Novex Bis–Tris system (Life Technologies) or hand-cast equivalents46. Light blue cathode buffer was used to facilitate visualization of the activity stain.Standard denaturing SDS-PAGE was used to estimate the molecular mass of thiaminases after denaturation. Denaturing SDS-PAGE was run using one of three relatively equivalent methods: pre-cast TGX gels (BioRad) according to the manufacturer’s instructions, hand-cast Tris–HCl gels using standard Laemmli chemistry47 with an operating pH of approximately 9.5, or hand-cast Bis–Tris gels (MOPS buffer) with an operating pH of approximately 7. For all denaturing and non-denaturing SDS-PAGE applications, standard Laemmli sample buffer was used, and samples were heated to 75 °C for 15 min to facilitate denaturation followed by brief centrifugation to eliminate any precipitated debris.Non-denaturing PAGE was used as an alternative to denaturing PAGE for the common carp thiaminase that could not be renatured (i.e., activity could not be recovered) following a denaturing SDS-PAGE. Non-denaturing PAGE was conducted using any of the three aforementioned gel chemistries with SDS-containing running buffers including reductant (DTT), but samples were not heated prior to application to the gel. Samples for non-denaturing PAGE were allowed to incubate in sample buffer at room temperature for 30 min prior to gel loading. This preserves the charge-shift induced by SDS but does not result in protein denaturation, facilitating in-gel analysis of thiaminase I activity after separation.To visualize proteins following electrophoresis, gels were stained with Coomassie stain (CBR-250 at 1 g/L in methanol/acetic acid/water (4:5:1) and destained with methanol/acetic acid/water (1.7:1:11.5). Mini-gels were run on BioRad’s mini-protean gel rigs. Midi-gels (16 cm length) were run on Hoefer’s SE660, and large-format gels (32 cm length) were run on a BioRad’s Protean Slab Cell. Mini-gels were generally run at room temperature, and midi- and large-format gels were run at 4 °C. Blue-native PAGE was always run at 4 °C.Two-dimensional electrophoresis (2DE) separated proteins in the first dimension based on pI and in the second dimension based on mass (either native or denatured). 2DE was performed by combining in-gel IEF with either denaturing SDS-PAGE, non-denaturing SDS-PAGE, or native PAGE. IPG strips were incubated in TRIS-buffered equilibration solution48 either with 6 M urea, SDS, and iodacetamide (denaturing) or without urea, SDS, and iodacetamide (non-denaturing) for 20 min. Low melting point agarose was used to solidify IGP strips in place. Agarose was cooled to just above the gelling temperature, as hot agarose inactivated thiaminase I activity.Isoelectric focusingIsoelectric focusing (IEF) was conducted both in-gel and in-liquid. In-gel IEF was conducted in immobilized pH gradient (IPG) strips using a Multifor II (GE Healthcare Life Sciences). Prior to rehydration, all protein preparations were desalted in low-salt (~ 5 to 10 mM) sodium or potassium phosphate buffer (pH 6.5) using 10 kDA MWCO filters. All samples were applied using sample volumes and protein concentrations recommended by the manufacturer. For standard denaturing in-gel IEF, rehydration solution consisted of 8 M urea, 2% CHAPS, 2% IPG buffer of the appropriate pH-range, 1% bromophenol blue, and 18 mM DTT. The IEF was conducted at maximum of 2 mA total current and 5 W total power, with an EPS3500 XL power supply in gradient mode. Voltage gradients were based on standard protocols recommended by the manufacturer. In-gel IEF was also performed under native conditions to allow thiaminase I activity staining of IPG strips. Protocols were essentially the same as those for denaturing conditions, with the following exceptions: (1) urea was eliminated and the CHAPS concentration was reduced to 0.5% in the rehydration solution; (2) rehydration was conducted at 14 °C; and (3) the water in the cooling tray was cooled to 4 °C.In-liquid IEF was conducted using a Rotofor (BioRad) according to the manufacturer’s instructions. Non-denaturing in-liquid IEF was also conducted using a focusing solution including no urea, 2% pH 3–10 biolyte, 0.5% CHAPS, 20% glycerol, and 5 mM DTT. The addition of glycerol helped retain activity but also increased focusing times. The Rotofor was run at a constant 15 W with a maximum current of 20 mA and voltage set for a maximum of 2000 V. Samples containing 8 M urea were cooled to 14 °C during focusing to avoid urea precipitation, whereas samples lacking urea were cooled to 4 °C during focusing. Protein extracts in salt solutions greater than 10 mM were desalted directly in focusing solution using a 10 kDA MWCO filter. Focusing runs were allowed to proceed until the voltage stabilized and fractions were harvested with the needle array and vacuum pump. Ampholytes were removed by addition of NaCl to 1 M and then samples were desalted into phosphate buffer using a 10kD MWCO filter.Thiaminase I activity measurementsFor quantitative measurements of thiaminase I activity, we conducted a radiometric assay at CERC as previously described49. Zebrafish homogenates were diluted 1:8, 1:16, or 1:32 in cold phosphate buffer, pH 6.5. Two replicates per dilution were assayed. Activity was calculated from the greatest dilution that gave activity within the linear range of the assay and was reported as pmol thiamine consumed per g tissue (wet weight) per minute (pmol/g/min).Thiaminase I activity stainingAfter electrophoresis, gels were stained for thiaminase I activity using a previously described diazo-coupling reaction19,50. Briefly, gels were washed 3 times in water, twice in 25 mM sodium phosphate buffer with 1 mM DTT, and once in 25 mM sodium phosphate buffer without DTT. Gels were then incubated in 0.89 mM thiamine-HCl and co-substrate (1.45 mM pyridoxine, 24 mM nicotinic acid, or 20 mM pyridine) in 25 mM sodium phosphate buffer for 10 min. Gels were briefly rinsed in water and placed in a lidded container and incubated at 37 °C for 30 min to allow thiamine degradation by any thiaminases in the gel. The diazo stain19,50 was then applied to detect remaining thiamine in the gel for five minutes with gentle agitation. Stained gels were rinsed with water and photographed, and further stained with Coomassie to visualize proteins. More

Evapotranspiration measurements and above-ground biomassFigure 1 shows daily evapotranspiration (ET, mm day−1) of each CC tested before mowing (DOY, day of the year, 184) and at 2, 8, 17 and 25 days after mowing (DOY 190, 196, 205 and 213); bare soil was also included as a reference. Before mowing, ET rates showed significant differences between and within the three groups. CR plants had a mean ET of 8.1 mm day−1, which was lower, compared to the other two groups (10.6 and 18.6 mm day−1 for GR and LE, respectively) and the bare soil control (8.5 mm day−1). On DOY 184, values as high as 9.4 (Glechoma hederacea L., GH) and 9.8 mm day−1 (Trifolium subterraneum L. cv. Denmark, TS) were found (Fig. 1), while ranging around 7 mm day-1, Dichondra repens J.R.Forst. & G.Forst. (DR), Hieracium pilosella L. (HP), and Sagina subulata (Swartz) C. Presl (SS) ET were lower than soil evaporation itself.Figure 1Vertical bars represent the daily water use as referred to unit of soil (ET, mm day−1) for the bare soil (yellow) and all the cover crop species as divided into creeping plants (shades of blue), legumes (shades of green) and grasses (shades of orange). Evapotranspiration was measured though a gravimetric method before (i.e. − 4) and at 2, 8, 17 and 25 days after mowing. ET data are mean values ± SE (n = 4).Full size imageOn the same day, a large ET variation was recorded within the GR group as Festuca arundinacea Schreb. cv. Thor (FA) scored the highest daily ET values (13.4 mm day−1), whereas in Festuca ovina L. cv. Ridu (FO), water loss was reduced by 45% (7.5 mm day−1). Within the 15 CCs, LE registered the highest pre-mowing ET with Trifolium michelianum Savi cv. Bolta (TM) peaking at 22.6 mm day−1. However, within LE, Medicago polymorpha L. cv. Scimitar (MP) showed ET values as low as 12.1 mm day−1 (Fig. 1).Two days after mowing, all tested CCs recorded ET values lower than 9 mm day−1 (Fig. 1). Moreover, water use reduction among LE ranged between 56% (M. polymorpha, MP) and 73% (T. michelianum, TM), such that T. michelianum (TM, 6.1 mm day−1), Medicago truncatula Gaertn. cv. Paraggio (MT, 5.6 mm day−1) and M. polymorpha (MP, 5.2 mm day−1) registered ET values lower than the bare soil (7.0 mm day−1). Even though registering a consistent ET reduction after mowing, GR retained ET rates slightly higher than bare soil, except for F. ovina (FO), which recorded the lowest at 6.3 mm day−1. Subsequent samplings showed that most of the CCs had a progressive recovery in water use (Fig. 1) and data taken 17 days after mowing confirmed that Lotus corniculatus L. cv. Leo (LC) and all GR fetched pre-mowing ET rates. Medicago lupulina L. cv. Virgo (ML) registered a partial recovery with similar rates (about 13 mm day−1) at 17 and 25 days after the mowing event. F. ovina and all remaining LE stayed below 10 mm day−1 with ET values close to the control until the end of the trial. At 17 days from grass cutting, under a quite high exceeding-the-pot biomass, both G. hederacea (GH) and T. subterraneum (TS) reached ET values as high as 12.0 and 11.4 mm day−1, respectively. On the other hand, D. repens (DR), H. pilosella (HP), and S. subulata (SS) even though with slightly higher ET values than those registered at the beginning of the trial (DOY 184), remained close to the soil evaporation rates until DOY 213.Aboveground dry clipped biomass at the first mowing date (ADW_MW1, DOY 188) showed large differences among groups, as represented in Table 1. ADW_MW1 within LE was quite variable, as values ranged between 274.3 g m−2 (M. polymorpha, MP) and 750.0 g m−2 (T. michelianum, TM). With a mean value of 565.9 g m−2, LE aboveground biomass was 80% higher than the mean GR ADW_MW1 (110.2 g m-2). F. ovina (FO) scored the lowest value at 48.4 g m−2 among grasses, while within the creeping group, G. hederacea (GH) and T. subterraneum (TS) had biomass development outside the pot edges totalling 89.6 g m−2 and 23.2 g m−2, respectively.Table 1 Aboveground dry biomass clipped at the first mowing event (ADW _MW1), the corresponding leaf area surface index (LAI) and water use per leaf area unit (ETLEAF) of all cover crops tested.Full size tableLeaf area index (LAI, m2 m−2) at mowing showed the highest values in LE with LAI peaking at 12.4 (Table 1). Among GR, LAI did not show significant differences, being around 1.2. Concerning CR, LAI was assessed at 0.2 and 0.8 for T. subterraneum (TS) and G. hederacea (GH) respectively, while LAI estimated through photo analysis ranged between 1.3 (D. repens, DR) and 3.6 (T. subterraneum TS).Evapotranspiration per leaf area unit (ETLEAF) was notably higher in GR, ranging between 7.75 (F. ovina, FO) and 9.22 (Lolium perenne L. cv. Playfast, LP) mm m−2 day−1 (Table 1). In descending order, ETLEAF was the highest in D. repens (DR, 5.46 mm m−2 day−1). Similar ETLEAF was found when comparing some LE and CR species such as M. truncatula (MT, 3.40 mm m−2 day−1), M. lupulina (ML, 4.05 mm m−2 day−1), G. hederacea (GH, 3.68 mm m−2 day−1), H. pilosella (HP, 3.86 mm m-2 day-1) and T. subterraneum (TS, 2.74 mm m−2 day−1). T. michelianum (TM), with 1.81 mm m-2 day-1 scored the lowest ETLEAF of all species (Table 1).Plotting LAI versus the before-mowing ET yielded a significant quadratic relationship (R2  > 0.76) (Fig. 2a) which helped to distinguish two different data clouds. Till LAI values of about 6, the model was linear, having at its lower end all GR and CR species with the inclusion of M. polymorpha (MP) as a legume, while, at the other end, M. truncatula (MT), L. corniculatus (LC) and M. lupulina (ML) were grouped together. T. michelianum (TM) was isolated from all CCs at 22.56 mm day−1.Figure 2Panel (a): quadratic regression of leaf area index (LAI, m2 m−2) vs cover crop evapotranspiration per unit of soil (ET, mm day−1). Each data point is mean value ± SE (n = 4). The quadratic model equation is y = − 0.128×2 + 2.9968x + 5.4716, R2 = 0.76. Panel (b): the quadratic regression between LAI corresponding to the clipped biomass (m2 m−2) and cover crop ET reduction (%). Each data point is mean value ± SE (n = 4). Quadratic model equation is y = − 0.8985×2 + 16.503x + 5.1491, R2 = 0.94.Full size imageWhen regressing the fraction of ET reduction, compared to pre-mowing values vs LAI (Fig. 2b), the same quadratic model achieved a very close fit (R2 = 0.94, p  1 mm) root diameters as affected by soil cover.Full size tableThe highest values of diameter class length (DCL, mm cm−3) for very fine roots (DCL_VF,  1.0 mm) roots although, most notably, L. corniculatus roots showed the highest abundance for both DCL_M (23.08 cm cm−3) and DCL_C (0.54 cm cm−3).At the 10–20 cm soil depth, GR confirmed the highest values for both very fine and fine roots, with F. arundinacea reaching maximum DCL of 2.269 and 5.215 cm cm-3, respectively (Table 2). L. corniculatus largely outscored any other species for both medium and coarse root diameter (6.173 and 0.037 cm cm−3, respectively), with F. arundinacea ranking second (3.157 and 0.016 cm cm−3, respectively).The highest root dry weight (RDW, mg cm-3) within the topsoil layer was reached by L. corniculatus (8.7 mg cm−3) and F. arundinacea (7.6 mg cm-3). Notably, such values were significantly higher than those recorded on the remaining species, except for the F. arundinacea vs F. rubra commutata comparison (Table 2). At 10–20 depth, scant variation was recorded in RDW measured in grasses, whereas L. corniculatus held its supremacy within legumes (4.5 mg cm−3). Within the creeping type, D. repens (DR) and G. hederacea (GH) scored RDW values as high as those determined for grass species (namely F. arundinacea , P. pratensis and F. rubra commutata), whereas S. subulata (SS) essentially had no root development.Soil aggregates and mean weight diameter (MWD)Table 3 reports the proportional aggregate weight (g kg−1) for both 0–10 and 10–20 cm soil depths. Compared to bare soil, the largest increase in large macroaggregates (LM,  > 2000 µm) in the top 10 cm of soil was achieved by L. corniculatus with 461 g kg−1. L. corniculatus differed from the rest of the LE group, whose grand mean (90 g kg−1) was the lowest of the three tested groups. As a legume, T. subterraneum (TS, 122 g kg−1) recorded the lowest values compared to fellow CR species, ranging between 211 (D. repens, DR) and 316 g kg−1 (G. hederacea, GH). GR recorded LM values slightly lower than those of CR, with a mean value of 217 vs 224 g kg-1.Table 3 Proportional aggregate weight (g kg−1) of sand-free aggregate-size fractions acquired from wet sieving as affected by soil cover and mean weight diameter (MWD). Aggregate-size fraction divided as macroaggregates with large size ( > 2 mm, LM) and small size (2 mm—250 μm, sM), microaggregates (250 μm—53 μm, m), and silt and clay ( More

Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73. https://doi.org/10.1038/nature22900 (2017).Article 
ADS 
CAS 

Google Scholar 
Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116. https://doi.org/10.1016/j.tree.2013.12.001 (2014).Article 

Google Scholar 
Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. J. Sci. Adv. 1, e1400253. https://doi.org/10.1126/sciadv.1400253 (2015).Article 
ADS 

Google Scholar 
Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, e197. https://doi.org/10.1371/journal.pbio.0020197 (2004).Article 

Google Scholar 
Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).Article 
ADS 
CAS 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 124–148 (2014).Article 

Google Scholar 
Bauer, H. et al. Lion (Panthera leo) populations are declining rapidly across Africa, except in intensively managed areas. Proc. Natl. Acad. Sci. 112, 14895–14899 (2015).Article 
ADS 

Google Scholar 
Bauer, H., Page-Nicholson, S., Hinks, A. & Dickman, A. Guidelines for the Conservation of lion in Africa 17–24 (IUCN SSC Cat Specialist Group, 2018).
Google Scholar 
Lindsey, P. A., Roulet, P. A. & Romanach, S. S. Economic and conservation significance of the trophy hunting industry in sub-Saharan Africa. Biol. Conserv. 134, 455–469. https://doi.org/10.1016/j.biocon.2006.09.005 (2007).Article 

Google Scholar 
Vucetich, J. A. et al. The value of argument analysis for understanding ethical considerations pertaining to trophy hunting and lion conservation. Biol. Conserv. 235, 260–272. https://doi.org/10.1016/j.biocon.2019.04.012 (2019).Article 

Google Scholar 
Dube, N. Voices from the village on trophy hunting in Hwange district, Zimbabwe. Ecol. Econ. 159, 335–343. https://doi.org/10.1016/j.ecolecon.2019.02.006 (2019).Article 

Google Scholar 
Murombedzi, J. African wildlife and livelihoods. In The Promise and Performance of Community Conservation (eds Hulme, D. & Murphree, M.) 244–255 (James Currey, 2001).
Google Scholar 
Leader-Williams, N., Baldus, R. D. & Smith, R. J. Recreational hunting. In Conservation and Rural Livelihoods (eds Dickson, B. et al.) 296–316 (Blackwell Publishing Ltd., 2009).Chapter 

Google Scholar 
DiMinin, E., Leader-Williams, N. & Bradshaw, C. J. A. Banning trophy hunting will exacerbate biodiversity loss. Trends Ecol. Evol. 31, 99–102 (2016).Article 

Google Scholar 
Whitman, K., Starfield, A. M., Quadling, H. S. & Packer, C. Sustainable trophy hunting of African lions. Nature 428, 175–178 (2004).Article 
ADS 
CAS 

Google Scholar 
Packer, C. et al. Sport hunting, predator control and conservation of large carnivores. PLoS ONE 4, e5941. https://doi.org/10.1371/journal.pone.0005941 (2009).Article 
ADS 
CAS 

Google Scholar 
Mweetwa, T. et al. Quantifying lion (Panthera leo) demographic response following a three-year moratorium on trophy hunting. PLoS ONE 13, e0197030. https://doi.org/10.1371/journal.pone.0197030 (2018).Article 
CAS 

Google Scholar 
Loveridge, A. J. et al. Conservation of large predator populations: Demographic and spatial responses of African lions to the intensity of trophy hunting. Biol. Conserv. 204, 247–254. https://doi.org/10.1016/j.biocon.2016.10.024 (2016).Article 

Google Scholar 
Starfield, A. M., Shiell, J. D. & Smuts, G. L. Simulation of lion control strategies in a large game reserve. Ecol. Model. 13, 17–28 (1981).Article 

Google Scholar 
Venter, J. & Hopkins, M. E. Use of a simulation model in the management of a lion population. S. Afr. J. Wildl. Res. 18, 126–130 (1988).
Google Scholar 
Starfield, A. M. & Bleloch, A. L. Modelling the effect of contraception on part of the lion population in Etosha National Park. Applied Mathematic Dept. Report R3/82, Witwaterstrand University, South Africa. 7 (1982).Dickman, A., Becker, M., Begg, C., Loveridge, A. J. & Macdonald, D. W. Guidelines for the Conservation of Lions in Africa, Ch. 6 69–75 (IUCN SSC Cat Specialist Group, 2018).
Google Scholar 
Creel, S. et al. Assessing the sustainability of lion trophy hunting with recomendations for policy. Ecol. Appl. 26, 2347–2357. https://doi.org/10.1002/eap.1377 (2016).Article 

Google Scholar 
Barthold, J., Loveridge, A. J., Macdonald, D. W., Packer, C. & Colchero, F. Bayesian estimates of male and female African lion mortality for future use in population management. J. Appl. Ecol. 53, 295–304 (2016).Article 

Google Scholar 
Loveridge, A. J., Valeix, M., Elliot, N. B. & Macdonald, D. W. The landscape of anthropogenic mortality: How African lions respond to spatial variation in risk. J. Appl. Ecol. 54, 815–825. https://doi.org/10.1111/1365-2664.12794 (2017).Article 

Google Scholar 
Loveridge, A. J. et al. Evaluating the spatial intensity and demographic impacts of wire-snare bush-meat poaching on large carnivores. Biol. Conserv. 244, 108504 (2020).Article 

Google Scholar 
Becker, M. S. et al. Estimating past and future male loss in three Zambian lion populations. J. Wildl. Manag. 77, 128–142 (2013).Article 

Google Scholar 
Kiffner, C., Meyer, B., Muhlenberg, M. & Waltert, M. Plenty of prey, few predators: What limits lions Panthera leo in Katavi National park, western Tanzania?. Oryx 43, 52–59 (2009).Article 

Google Scholar 
Loveridge, A. J., Searle, A. W., Murindagomo, F. & Macdonald, D. W. The impact of sport hunting on the population dynamics of an African lion population in a protected area. Biol. Conserv. 134, 548–558 (2007).Article 

Google Scholar 
Miller, J. R. B. et al. Aging traits and sustainable trophy hunting of African lions. Biol. Conserv. 201, 160–168 (2016).Article 

Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
ADS 
CAS 

Google Scholar 
Gervasi, V., Linnell, J. D. C., Brøseth, H. & Gimenez, O. Failure to coordinate management in transboundary populations hinders the achievement of national management goals: The case of wolverines in Scandinavia. J. Appl. Ecol. 56, 1905–1915. https://doi.org/10.1111/1365-2664.13379 (2019).Article 

Google Scholar 
Breitenmoser, U. & Nobbe, C. Guidelines for the Conservation of Lions in Africa (ed IUCN CSG/SSC) 29–30 (IUCN, 2018).du Preez, B. & Lopez-Bao, J. V. Guidelines for the Conservation of the Lion in Africa (ed IUCN CSG/SSC) 76–78 (IUCN, 2018).Loveridge, A. J., Hemson, G., Davidson, Z. & Macdonald, D. W. African lions on the edge: reserve boundaries as ‘attractive sinks’ In Biology and Conservation of Wild Felids, Ch. 11 (eds Macdonald, D. W. & Loveridge, A. J.) 283–304 (Oxford University Press, London, 2010).

Google Scholar 
Borrego, N., Ozgul, A., Slotow, R. & Packer, C. Lion population dynamics: Do nomadic males matter?. Behav. Ecol. 29, 660–666. https://doi.org/10.1093/beheco/ary018%JBehavioralEcology (2018).Article 

Google Scholar 
Packer, C. et al. The case for fencing remains intact. Ecol. Lett. https://doi.org/10.1111/ele.12171 (2013).Balme, G. et al. Big cats at large: Density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267. https://doi.org/10.1002/1438-390x.1023 (2019).Article 

Google Scholar 
Grünewald, C., Schleuning, M. & Böhning-Gaese, K. Biodiversity, scenery and infrastructure: Factors driving wildlife tourism in an African savannah national park. Biol. Conserv. 201, 60–68. https://doi.org/10.1016/j.biocon.2016.05.036 (2016).Article 

Google Scholar 
Pulliam, H. R. Sources, sinks, and population. Regulation 132, 652–661. https://doi.org/10.1086/284880 (1988).Article 

Google Scholar 
Lamb, C. T. et al. The ecology of human–carnivore coexistence. Proc. Natl. Acad. Sci. 117, 17876–17883. https://doi.org/10.1073/pnas.1922097117 (2020).Article 
ADS 
CAS 

Google Scholar 
Robinson, H. S., Weilgus, R. B., Cooley, H. & Cooley, S. Source—sink populations in carnivore management: cougar demography and immigration in a hunted population. Ecol. Appl. 18, 1028–1037 (2008).Article 

Google Scholar 
Creel, S. et al. Questionable policy for large carnivore hunting. Science 350, 1473–1475 (2015).Article 
ADS 
CAS 

Google Scholar 
Cushman, S. A. et al. Prioritizing core areas, corridors and conflict hotspots for lion conservation in southern Africa. PLoS ONE 13, e0196213. https://doi.org/10.1371/journal.pone.0196213 (2018).Article 
CAS 

Google Scholar 
Kelly, M. J. & Durant, S. M. Viability of the Serengeti cheetah population. Conserv. Biol. 14, 786–797 (2000).Article 

Google Scholar 
Skalski, J. R., Ryding, K. & Millspaug, J. J. Wildlife Demography: Analysis of Sex, Age, and Count Data (Elsevier Academic Press, 2005).
Google Scholar 
Hamlin, K. L., Pac, D. F., Sime, C. A., DeSimone, R. M. & Dusek, G. L. Evaluating the accuracy of ages obtained by two methods for montana ungulates. J. Wildl. Manag. 64, 441–449. https://doi.org/10.2307/3803242 (2000).Article 

Google Scholar 
Storm, D. J. et al. Estimating ages of white-tailed deer: Age and sex patterns of error using tooth wear-and-replacement and consistency of cementum annuli. Wildl Soc Bull 38, 849–856. https://doi.org/10.1002/wsb.457 (2014).Article 
ADS 

Google Scholar 
Balme, G. A., Hunter, L. & Braczkowski, A. R. Applicability of age-based hunting regulations for African Leopards. PLoS ONE 7, e35209. https://doi.org/10.1371/journal.pone.0035209 (2012).Article 
ADS 
CAS 

Google Scholar 
Gipson, P. S., Ballard, W. B., Nowak, R. M. & Mech, L. D. Accuracy and precision of estimating age of gray wolves by tooth wear. J. Wildl. Manag. 64, 752–758. https://doi.org/10.2307/3802745 (2000).Article 

Google Scholar 
Hiller, T. L. Comparison of two age-estimation techniques for cougars. J. Northwest. Nat. 77–82, 76 (2014).
Google Scholar 
Begg, C. M., Miller, J. R. B. & Begg, K. S. Effective implementation of age restrictions increases selectivity of sport hunting of the African lion. J. Appl. Ecol. 55, 139–146. https://doi.org/10.1111/1365-2664.12951 (2018).Article 

Google Scholar 
Mandisodza-Chikerema, R., Jooste, D. & Funston, P. J. Lion aging and adaptive quota management report: Ages of lions hunted and recommended quotas for 2019 in Zimbabwe. 12 (Unpublished report, Zimbabwe Parks and Wildlife Management and Panthera, Harare, Zimbabwe, 2019).Smuts, G. L., Anderson, J. L. & Austin, J. C. Age determination of the African lion (Panthera leo). J. Zool. Lond. 185, 115–146 (1978).Article 

Google Scholar 
Lindsey, P. A. et al. The trophy hunting of African lions: Scale, current management practices and factors undermining sustainability. PLoS ONE 8, 1–11 (2013).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Packer, C. et al. Effects of trophy hunting on lion and leopard populations in Tanzania. Conserv. Biol. 25, 142–153 (2011).Article 
CAS 

Google Scholar 
Mace, G. M. & Reynolds, J. Exploitation as a conservation issue. In Conservation of Exploited Species, Ch. 1 (eds Reynolds, J. et al.) 3–15 (Cambridge University Press, Cambridge, 2001).
Google Scholar 
Struhsaker, T. T. A biologists perspective on the role of sustainable harvest in conservation. Conserv. Biol. 12, 930–932 (1998).Article 

Google Scholar  More